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PubMed2000

Machine learning for survival analysis: a case study on recurrence of prostate cancer.

Machine learning techniques have recently received considerable attention, especially when used for the construction of prediction models from data. Despite their potential advantages over standard statistical methods, like their ability to model non-linear relationships and construct symbolic and interpretable models, their applications to survival analysis are at best rare, primarily because of the difficulty to appropriately handle censored data. In this paper we propose a schema that enables the use of classification methods--including machine learning classifiers--for survival analysis. To appropriately consider the follow-up time and censoring, we propose a technique that, for the patients for which the event did not occur and have short follow-up times, estimates their probability of event and assigns them a distribution of outcome accordingly. Since most machine learning techniques do not deal with outcome distributions, the schema is implemented using weighted examples. To show the utility of the proposed technique, we investigate a particular problem of building prognostic models for prostate cancer recurrence, where the sole prediction of the probability of event (and not its probability dependency on time) is of interest. A case study on preoperative and postoperative prostate cancer recurrence prediction shows that by incorporating this weighting technique the machine learning tools stand beside modern statistical methods and may, by inducing symbolic recurrence models, provide further insight to relationships within the modeled data.

Artificial Intelligence↗

PubMed2025

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data, which is pivotal to understanding the nature of complex molecular systems. Here, we show how to construct a simple model using automated machine learning (AutoML) to predict protein abundance in Schizosaccharomyces pombe, using data obtained from codon usage bias and quantitative proteomics.

Machine Learning↗

PubMed2025

The signed two-space proximity model for learning representations in protein-protein interaction networks.

MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. RESULTS: S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns. AVAILABILITY: S2-SPM is implemented and freely available under the MIT license at https://github.com/Nicknakis/S2SPM.

Protein Interaction Mapping↗

PubMed2001

Spike-timing-dependent Hebbian plasticity as temporal difference learning.

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physio-logically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

Action Potentials↗

PubMed2004

Predicting genetic regulatory response using classification.

MOTIVATION: Studying gene regulatory mechanisms in simple model organisms through analysis of high-throughput genomic data has emerged as a central problem in computational biology. Most approaches in the literature have focused either on finding a few strong regulatory patterns or on learning descriptive models from training data. However, these approaches are not yet adequate for making accurate predictions about which genes will be up- or down-regulated in new or held-out experiments. By introducing a predictive methodology for this problem, we can use powerful tools from machine learning and assess the statistical significance of our predictions. RESULTS: We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences ('motifs') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment ('parents'). Thus, our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. We convert the regression task of predicting real-valued gene expression measurements to a classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S.cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. We also show how to extract significant regulators, motifs and motif-regulator pairs from the learned models for various stress responses. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks. AVAILABILITY: The MLJava package is available upon request to the authors. Supplementary: Additional results are available from http://www.cs.columbia.edu/compbio/geneclass

Binding Sites↗

PubMed1989

Causal explanations and emotional health of women during divorce.

This study investigated (a) relationships between women's causal explanations for divorce and their present emotional health, and (b) the clinical usefulness of the Personal Stress Inventory (Ireton, 1980) as a stress assessment tool. The convenience sample consisted of 36 women ending a first marriage. Subjects were interviewed using the Tennessee Self-Concept Scale (Fitts, 1965), the Personal Stress Inventory, and a causal explanations questionnaire. Results indicated that causal explanations did predict self-esteem and emotional distress under conditions suggested by the reformulated learned helplessness model. The Personal Stress Inventory includes an emotional distress subscale that predicted self-esteem, self-perceptions of coping ability, and overall concerns about wellness/energy.

Adaptation, Psychological↗

PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗

PubMed2002

The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves.

Learning curves are presented as an unbiased means for evaluating the performance of models for neuroimaging data analysis. The learning curve measures the predictive performance in terms of the generalization or prediction error as a function of the number of independent examples (e.g., subjects) used to determine the parameters in the model. Cross-validation resampling is used to obtain unbiased estimates of a generic multivariate Gaussian classifier, for training set sizes from 2 to 16 subjects. We apply the framework to four different activation experiments, in this case [(15)O]water data sets, although the framework is equally valid for multisubject fMRI studies. We demonstrate how the prediction error can be expressed as the mutual information between the scan and the scan label, measured in units of bits. The mutual information learning curve can be used to evaluate the impact of different methodological choices, e.g., classification label schemes, preprocessing choices. Another application for the learning curve is to examine the model performance using bias/variance considerations enabling the researcher to determine if the model performance is limited by statistical bias or variance. We furthermore present the sensitivity map as a general method for extracting activation maps from statistical models within the probabilistic framework and illustrate relationships between mutual information and pattern reproducibility as derived in the NPAIRS framework described in a companion paper.

Adult↗

PubMed2000

Distance-based reconstruction of tree models for oncogenesis.

Comparative genomic hybridization (CGH) is a laboratory method to measure gains and losses in the copy number of chromosomal regions in tumor cells. It is hypothesized that certain DNA gains and losses are related to cancer progression and that the patterns of these changes are relevant to the clinical consequences of the cancer. It is therefore of interest to develop models which predict the occurrence of these events, as well as techniques for learning such models from CGH data. We continue our study of the mathematical foundations for inferring a model of tumor progression from a CGH data set that we started in Desper et al. (1999). In that paper, we proposed a class of probabilistic tree models and showed that an algorithm based on maximum-weight branching in a graph correctly infers the topology of the tree, under plausible assumptions. In this paper, we extend that work in the direction of the so-called distance-based trees, in which events are leaves of the tree, in the style of models common in phylogenetics. Then we show how to reconstruct the distance-based trees using tree-fitting algorithms developed by researchers in phylogenetics. The main advantages of the distance-based models are that 1) they represent information about co-occurrences of all pairs of events, instead of just some pairs, 2) they allow quantitative predictions about which events occur early in tumor progression, and 3) they bring into play the extensive methodology and software developed in the context of phylogenetics. We illustrate the distance-based tree method and how it complements the branching tree method, with a CGH data set for renal cancer.

Algorithms↗

PubMed2001

Seeing mum drinking a 'light' product: is social learning a stronger determinant of taste preference acquisition than caloric conditioning?

OBJECTIVE: It was examined whether caloric conditioning or social learning strategies dominate in taste preference acquisition in children. The caloric learning paradigm predicts that eating or drinking artificially sweetened products, which deliver virtually no energy, will not lead to a taste preference whereas the social learning paradigm predicts that seeing important others modelling the eating and drinking of these 'light' products will induce a preference for the taste of light products in the child. DESIGN: In a 2 x 2 between subjects factorial design, the amount of energy and social modelling was varied. SETTING: The study was undertaken at primary schools in Maastricht, The Netherlands. SUBJECTS: Forty-five children participated and six children dropped out. The 39 children who completed the study (14 boys and 25 girls) had a mean age of 67 months (range 51--81, s.d. 5.6). INTERVENTIONS: Each subject took part in nine conditioning trials with an individually selected tasting yoghurt which was not preferred very much at the pre-test. RESULTS: The children in the combined caloric and social condition showed an increase in their preference for the conditioned taste which was larger than a regression-to-the-mean effect (P=0.007), whereas children in the other groups did not. CONCLUSION: Caloric and social learning combined, ie modelling the consumption of energy-rich foods or drinks, is the best way to establish taste preferences. Children more easily learn a preference for energy-rich food that is eaten by significant others than for food that is low in energy and eaten by significant others.

Child↗

PubMed2000

Volume of focal brain lesions and hippocampal formation in relation to memory function after closed head injury in children.

OBJECTIVES: (1) A study of verbal learning and memory in children who had sustained a closed head injury (CHI) at least 3 months earlier. (2) To relate memory function to focal brain lesion and hippocampal formation volumes using morphometric analysis of MRI. METHODS: A group of 245 children who had been admitted to hospital for CHI graded by the Glasgow coma scale (GCS), including 161 patients with severe and 84 with mild CHI completed the California verbal learning test (CVLT) and underwent MRI which was analysed for focal brain lesion volume independently of memory test data. Brain MRI with 1.5 mm coronal slices obtained in subsets of 25 patients with severe and 25 patients with mild CHI were analysed for hippocampal formation volume. Interoperator reliability in morphometry was satisfactory. RESULTS: Severity of CHI and age at study significantly affected memory performance. Regression analysis showed that bifrontal, left frontal, and right frontal lesion volumes incremented prediction of various learning and memory indices after entering the GCS score and age into the model. Extrafrontal lesion volume did not contribute to predicting memory performance. CONCLUSIONS: Prefrontal lesions contribute to residual impairment of learning and memory after severe CHI in children. Although effects of CHI on hippocampal formation volume might be difficult to demonstrate in non-fatal paediatric CHI, further investigation using functional brain imaging could potentially demonstrate hippocampal dysfunction.

Adolescent↗

PubMed1996

A sequence predicting CA3 is a flexible associator that learns and uses context to solve hippocampal-like tasks.

The model discussed in this paper is, by hypothesis, a minimal, biologically plausible model of hippocampal region CA3. Because cognitive mapping can be viewed as a sequence prediction problem, we qualify this model as a successful sequence predictor. Since the model solves problems which require the use of context, the model is also able to learn and use context. The model also solves configural learning problems of which, at least one, requires a hippocampus. Thus, by solving sequence problems, by solving configural learning problems, and by creating codes for context, this model provides a computational unification of hippocampal functions which are often viewed as disparate.

Cognition↗

PubMed2005

Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification.

When adapting to novel dynamic environments the nervous system learns to anticipate the imposed forces by forming an internal model of the environmental dynamics in a process driven by movement error reduction. Here, we tested the hypothesis that motor learning could be accelerated by transiently amplifying the environmental dynamics. A novel dynamic environment was created during treadmill stepping by applying a perpendicular viscous force field to the leg through a robotic device. The environmental dynamics were amplified by an amount determined by a computational learning model fit on a per-subject basis. On average, subjects significantly reduced the time required to predict the applied force field by approximately 26% when the field was transiently amplified. However, this reduction was not as great as that predicted by the model, likely due to nonstationarities in the learning parameters. We conclude that motor learning of a novel dynamic environment can be accelerated by exploiting the error-based learning mechanism of internal model formation, but that nonlinearities in adaptive response may limit the feasible acceleration. These results support an approach to movement training devices that amplify rather than reduce movement errors, and provide a computational framework for both implementing the approach and understanding its limitations.

Adaptation, Physiological↗

PubMed2006

Ventral-striatal/nucleus-accumbens sensitivity to prediction errors during classification learning.

A prominent theory in neuroscience suggests reward learning is driven by the discrepancy between a subject's expectation of an outcome and the actual outcome itself. Furthermore, it is postulated that midbrain dopamine neurons relay this mismatch to target regions including the ventral striatum. Using functional MRI (fMRI), we tested striatal responses to prediction errors for probabilistic classification learning with purely cognitive feedback. We used a version of the Rescorla-Wagner model to generate prediction errors for each subject and then entered these in a parametric analysis of fMRI activity. Activation in ventral striatum/nucleus-accumbens (Nacc) increased parametrically with prediction error for negative feedback. This result extends recent neuroimaging findings in reward learning by showing that learning with cognitive feedback also depends on the same circuitry and dopaminergic signaling mechanisms.

Basal Ganglia↗

PubMed1996

Text Learning Using Scientific Diagrams: Implications for Classroom Use

In two experiments, eighth-graders viewed ecologically valid diagrams and then read a text containing multiple feature-to-fact associations or studied the same materials in reverse order. Using the Kulhavy and Stock model of text learning using organized spatial displays, it was predicted that those students viewing the diagram prior to reading the text would recall more facts and features than subjects viewing the material in the reverse order. These predictions were well supported. In addition, since the materials used in the experiments were created from actual classroom materials, these experiments examined whether the model for text learning is also valid using this type of materials. In this area the model proved to be very effective as well.

Journal Article↗

PubMed1997

Evidence-Based Pattern Classification: A Structural Approach to Human Perceptual Learning and Generalization

Models of human pattern classification have been traditionally based on implicit pattern descriptions which involve lists of continuous attribute values or discrete features. Here we propose an alternative approach which makes explicit use of pattern structure in terms of components and their unary (part-specific) and binary (part-relational) properties. Such attributes "evidence" different classes of patterns and allow one to model processes of both perceptual learning and generalization to novel instances. An object in an evidence-based system is represented by a set of rules, where each rule provides a certain amount of class-specific evidence. The accumulated class evidence over all activated rules determines the classification probability. We have examined how well this concept reflects human performance by training observers to classify compound Gabor patterns and then testing them with segmented (grey-level-transformed) versions of the patterns in the original training set. If the observers were to construct rules to define each pattern class in terms of perceived parts and their relations, then it should be expected that classification performance would generalize to these new patterns. Results confirm this hypothesis and the specific feature extraction, learning, and rule generation model used to predict performance. Copyright 1997 Academic Press

Journal Article↗

PubMed2003

Using an integrated approach to understand vaccination behavior among young men who have sex with men: stages of change, the health belief model, and self-efficacy.

Studies continue to show that the majority of men who have sex with men (MSM) in the United States remain unvaccinated against hepatitis A (HAV); such limited vaccination coverage is a missed opportunity for preventing disease. This study sought to identify beliefs and attitudes associated with motivational readiness for vaccination against HAV among MSM, using a theoretically-integrated framework. Questionnaire data were collected from 358 MSM through bar outreach. MSM with increased readiness to complete the two-dose series perceived lower practical barriers and reported greater healthcare provider communication about their sexual orientation and risk behavior. They also perceived higher benefits to vaccination and higher severity of infection, and had higher self-efficacy to complete the vaccine series. Relationships between stages of change and theory-based constructs from the health belief model and the social learning theory follow predicted patterns suggesting that these theories may provide useful frameworks for understanding vaccination readiness and intervention strategy development among MSM.

Adolescent↗

PubMed2026

Flexible use of conserved motifs constrains genome access in cell type evolution.

Cell types can be organized into related families, but the regulatory mechanisms that define and maintain these families across deep evolutionary time remain unknown. Here, combining single-nucleus multi-omic sequencing with deep learning to analyse the accessible genomes of two groups of vastly divergent animals including flatworms and vertebrates, we find that hundreds of accessibility-dictating sequence motifs partition into distinct yet conserved sets, or 'vocabularies', each associated with a specific cell type family. However, combinatorial relationships among these motifs preferred by individual cell types are largely species specific. Deep-learning models trained on one species accurately predict family-level chromatin accessibility in distantly related species, albeit frequently rely on different motifs from shared vocabularies to reach convergent predictions. By contrast, models trained on individual cell types within a family lose cross-species predictive power, indicating that the regulatory syntax governing cell type-level identity evolves rapidly. We propose a 'collective maintenance' model in which motif vocabularies defining cell type families are evolutionarily stable, while recombination of these motifs generates cell type-specific regulatory programmes. This suggests that family identity is maintained collectively by large, conserved pools of regulatory factors, analogous to the logic of developmental homology, where character identity persists through network-level conservation despite extensive rewiring.

Journal Article↗